Method, apparatus and computer program product for assessment of attentional impairments

ABSTRACT

A method, apparatus, and computer useable medium that provides, among other things, a standardized test protocol for screening and evaluation of attentional impairment using EEG data. Further, the method, apparatus, and computer program product enhances existing psychological, behavioral, and physiological EEG data acquisition systems by introducing a sequential stochastic model procedure, and an intelligent data interpretation component capable of assessing EEG inconsistencies associated attentional impairments. Potential users of this product will be any person or organization that diagnoses or treats persons with attentional or cognitive impairments. The method can be used for initial screening and diagnosis of disorders associated with impaired attention, such as ADHD, as well as for treatment and evaluation of the effects of treatments, such as medication or additional therapies.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present invention is a division under 35 U.S.C. §120 of co-pendingapplication Ser. No. 10/467,826 filed Nov. 3, 2003 as a national stageof International Application No. PCT/US2002/0014188 filed May 6, 2002,which claims priority under 35 U.S.C. §119(e) from U.S. ProvisionalPatent Application Ser. No. 60/288,654 filed May 4, 2001, entitled “TheConsistency Index—An EEG Marker of Attention Deficit HyperactivityDisorder,” 60/360,295 filed Feb. 27, 2002, entitled “Method andApparatus for Assessment of Attentional Impairments: APsycho-Physiological Procedure,” and 60/367,894 filed on Mar. 26, 2002,entitled “Method and Apparatus for Assessment of AttentionalImpairments: A Psycho-Physiological Procedure,” the entire disclosuresof which are hereby incorporated by reference herein in their entirety.

FIELD OF THE INVENTION

The present invention relates to the assessment of individuals withattentional impairments, and more particularly an apparatus and methodfor using electroencephalographic (EEG) data for making various types ofassessments pertaining to various types of attentional impairments.

BACKGROUND OF THE INVENTION

Impairments in cognitive ability and attention are pervasive andpotentially debilitating components of many disorders, conditions,injuries and diseases, including mild cognitive impairment (MCI) inpersons with pre-dementia, dementia, dementia with Lewy bodies,Alzheimer's Disease, traumatic brain injury, AttentionDeficit/Hyperactivity Disorder (ADHD), and cognitive/attentionaldeclines associated with chronic diseases such as diabetes,cardiovascular disease, and HIV infection [1, 2, 3, 4, 5, 6, 7, 8]. Mostof these disorders are assumed to be pathology-based and thereforeamenable to intervention, especially if diagnosed early. Despite thestaggering number of such conditions, the significance of such cognitiveand attentional impairments in these conditions, and the importance ofearly, accurate, and comprehensive assessment and diagnosis, there iscurrently no such procedure or set of standards to employ to quantifysuch impairments, either when diagnosing the disorder or examiningeffectiveness of treatment.

For example, the recent NIH Consensus Statement on AttentionDeficit/Hyperactivity Disorder [9] concluded that ADHD is difficult todiagnose, considered a common problem, and is associated with manynegative consequences, both for the patient and society, and has beeninconsistently associated with neuroimaging and EEG anomalies that havebeen non-diagnostic in nature.

ADHD is one of multiple disorders associated with impairments inattention. Although this document may particularly identify attentionaldisorders associated with ADHD, the present invention shall be appliedto any disorder with associated attentional impairments. With respect todementia, recent research and a review of the literature conclude thatthe frequency of post stroke dementia and cognitive decline variedsharply when different systems of diagnostic classification and methodswere used [10]. Furthermore, recent findings support the need forvalidation not only of the criteria, but also the need for validatedmeasures to diagnosis dementia and cognitive impairment post stroke [10,11, 12], and Alzheimer's disease [13]. In addition, cognitiveabnormalities commonly occur in patients with HIV infection [14]. Amongotherwise healthy HIV-positive patients, cognitive deficits are thoughtto be infrequent [15], but some investigators suggest that moresensitive measures may be needed to detect the mild cognitive declineduring the asymptomatic stage [16].

Diagnostic Dilemma

There are numerous disorders and diseases associated with impairment ofattention and cognitive functioning, however, the diagnosis andquantification of impairment of attention in any disease or disorder istypically difficult. Some examples include: attentional impairmentsassociated with ADHD, HIV infection, Alzheimer's Disease, cardiovasculardisease, diabetes, and dementia.

With respect to ADHD, the DSM-IV [17] states “The essential features ofADHD is a persistent pattern of inattention and/orhyperactivity-impulsivity that is more frequent and severe than istypically observed in individuals in a comparable level of development.”Evidence of six of nine inattentive behaviors and/or six of ninehyperactive-impulsive behaviors must have been present before age seven,and must clearly interfere with social, academic and/or occupationalfunctioning. Consequently, the diagnosis of ADHD is highly dependent ona retrospective report of a patient's past behavior and subjectivejudgments on degree of relative impairment. Due to the subjective natureof assessment, precision in diagnosis has been elusive. ADHD is complexand influences all aspects of a person's life. It can co-exist withand/or mimic a variety of health, emotional, learning, cognitive, andlanguage problems. An appropriate, comprehensive evaluation for ADHDincludes a medical, educational, and behavioral history, evidence ofnormal vision and hearing, recognition of systemic illness, and adevelopmental survey. The diagnosis of ADHD should never be made basedexclusively on rating scales, questionnaires, or tests [18].

Prevalence

Since ADHD cannot be strictly defined, and precisely and objectivelymeasured, its true prevalence cannot be accurately determined. While theDSM-IV estimates the prevalence of ADHD in school-age children asbetween three percent and five percent, other community survey studiessuggest it may be as high as 16 percent [19]. ADHD occurs more commonlyin males than in females, with ratios ranging from 4:1 to 9:1. Of allchild referrals for mental health services, one-third to one-half isthought to be attributable to ADHD.

According to recent projections [20], Alzheimer's disease will affectincreasing numbers of people as baby boomers (persons born between 1946and 1964) age. The annual number of incident cases is expected to morethan double by the midpoint of the twenty-first century: from 377,000 in1995 to 959,000 in 2050. The proportion of new onset cases that are age85 or older will increase from forty percent in 1995 to 62 percent in2050.

It is clear from the number of persons suffering from attentional orcognitive difficulties or deficits, that there is a need for accuratediagnosis and validation of treatment efficacy. It is also clear thatthe portion of the population who will be suffering from cognitivedecline or impairment will continue to increase with the overall agingof the population and the increased diagnosis of attentional disorders.There is therefore a need in the art for a comprehensive, flexible, andan effective diagnostic measure of attentional abilities.

Negative Consequences

The hallmarks of ADHD are hyperactivity, impulsivity, and an inabilityto sustain attention. The DSM-IV distinguishes three types:predominantly inattentive type, predominantly hyperactive-impulsivetype, and combined type. In addition to the core clinical symptoms ofADHD, high levels of co-morbidity have been found with learning,oppositional defiant, conduct, mood, and anxiety disorders. Furthermore,it is estimated that the majority of children diagnosed with ADHDexhibit significant behavioral problems during adolescence and manifestcontinuing functional deficits and psychopathology into adulthood. Onereal-life consequence of ADHD is a five-fold increase in automobilecrashes [21].

Early diagnosis and treatment of Alzheimer's disease, dementia, andadditional progressive disorders associated with attentional impairmentis especially important because patients with early stages of dementiamay show reversal of their cognitive deficits and neurochemistryabnormalities after treatment [8].

Neuroimaging and EEG Findings Related to ADHD

In spite of these well-documented problems, the mechanisms and etiologyof ADHD remain methodologically difficult to study, with differentstudies yielding inconsistent results. Most investigators accept thatADHD exists as a distinct clinical syndrome and suggest a multifactorialetiology that includes neurobiology as an important factor. Zametkin andRapoport [22] identified eleven separate neuroanatomical hypotheses thathave been proposed for the etiology of ADHD. Most studies have concludedthat either delayed maturation or defects in cortical activation playlarge roles in the pathophysiology of ADHD. For example, studies ofcerebral blood flow determined by single-positron emission computerizedtomography have demonstrated decreased metabolic activity in suspectedattentional areas of the brain [23]. These, as well as additionalneurophysiological findings, have been interpreted as evidence ofdelayed maturation and cortical hypoarousal in regions of the prefrontaland frontal cortex, the two predominate etiological theories underlyingADHD. Unfortunately, while neuroanatomical findings lend support to thenotion that ADHD is a distinct clinical syndrome and add to ourunderstanding of the etiology of ADHD, neuroimaging techniques are tooexpensive, restricted to a few centers, and lack clear specificity andsensitivity in diagnosis of ADHD. There is therefore a need in the artfor an inexpensive and clear system and method for diagnosis of ADHS andother impairments.

There are a few basic methods of analyzing EEG data that have beenemployed in previous research—visual inspection of raw data andquantitative analyses of EEG data, including spectral and coherencemethods of analysis. To date, none of these methods have revealedpervasive or consistent patterns of EEG abnormalities with sufficientspecificity or sensitivity to separate children with ADHD from normalsubjects. The first of these methods of analysis involves visualinspection of raw EEG data. As long ago as 1938, Jasper, Solomon, andBradley, used this method and reported EEG abnormalities in childrenwith minimal brain dysfunction (an outdated term used to describechildren with hyperactivity and poor attentiveness as well as learningdisabilities and conduct disorder). In parallel with the development ofthe computer, researchers have applied a second method of EEG analysisemploying quantitative techniques. Quantitative EEG is a mathematicalanalysis of voltage-time series data with the intention of extractinguseful information not readily apparent to visual inspection. Spectralanalysis is a common technique of quantitative EEG. It mathematicallytransforms, via Fast Fourier Transform (FFT), raw amplitude-time datainto its component frequencies. During the 1970s several laboratoriesutilized a combination of visual inspection and quantitative techniques,and reported differences between the EEGs of hyperactive and normalchildren. Among the differences discovered were: a higher percentage ofabnormal EEG patterns (abnormal usually meaning excessive slow waveactivity) in clinical subjects than in controls; more power in the 0 to8 Hz spectrum in hyperactive children compared to normal controls; lesspower in the 10 Hz range for hyperactives than controls; and less betaand weaker stimulus-locked alpha attenuation in hyperactive than innon-hyperactive children. These early studies were typically confoundedby inconsistent and often inadequate assessment procedures andmethodologies. It is therefore not surprising that early researchdemonstrated no pervasive or consistent patterns of EEG data todiscriminate hyperactive, inattentive, or impulsive children fromcontrols.

Noticeably absent in the literature of that time, however, wasinformation concerning extensive EEG frequency components obtained fromseveral groups of clinical and control children engaged in tasksmanipulating attention. Numerous investigators have reported that onlywhen subjects are engaged in behavioral paradigms (particularly thosemanipulating attention) do electrophysiological differences appearbetween normal and hyperactive or LD children. Partially in response tothis deficit in the research literature, Dykman et al. [24] investigatedthe EEGs of four groups of boys (10 hyperactive, 10 learning-disabled,10 with both hyperactivity and LD) engaged in a complex visual searchtask. Spectral analysis of EEG data indicated that LD boys, hyperactiveboys, and boys with a mixed diagnosis displayed less beta and lessstimulus-locked alpha attenuation than normal boys. Thus, research inthe 1980s-1990s began to address and correct issues of uniformity ofdiagnosis, methodology, and accuracy in EEG acquisition, both in termsof theoretical understanding and technical application. In an attempt toclarify some of the EEG differences between hyperactive and normalsubjects, Satterfield, Schell, Backs & Hidaka [25] considered the impactof age upon EEG in two groups of normal (n=60) and hyperactive,inattentive, and impulsive males (n=138) ages 6-12, by examiningfollow-up EEGs on a subset of the hyperactive and normal subjects fouryears after the initial EEG. Their findings indicate that EEG powerspectral intensities of normal male children decrease with increasingage. However, EEG power declines slower with increasing age inhyperactive subjects. Overall, instead of clarifying the issues,Satterfield et al. conclude that “ . . . electrophysiologicaldifferences between hyperactive and normal male children are complex andvary markedly with age.” They further warn that “Computation of groupaverages which include data from children of a wide age range mayobscure rather than clarify the electrophysiological correlates of thisdisorder.”

More recent studies employing spectral analysis of EEG have also shownvarying patterns of EEG activity in ADHD subjects. Mann, et al. [26]tested 25 nine to twelve year-old boys with predominantlyinattentive-type ADHD, and found increased theta at both absolute andrelative percent power calculations, and decreased beta in temporal andfrontal sites. Janzen et al. [27] compared EEG differences between eightADD males and eight normal control males ages 9-12. Results demonstratedthat the ADD males had higher theta amplitudes for all sites. However,unlike Mann et al., Janzen et al. found no differences between groupsfor beta-all amplitudes. Clarke et al. [28] performed automated EEG onsubjects (ages 8-12) classified into groups of 20 ADHD-Combined Type, 20ADHD-Predominantly Inattentive Type, and 20 controls. Overall, theyfound evidence of increased absolute and relative theta in all ADHDsubjects, with the ADHD combined type showing a significantly greateramount of theta power than the predominantly inattentive-type. Inaddition, Clarke et al. found a decrease in alpha activity, but elevatedtheta present in all brain regions measured and not confined to frontalregions as previous studies had reported. In contrast to Mann, et al.they report less posterior absolute beta power in posterior regions. Inan interesting study by Ackerman, et al. [29] a group of 56 ADD/ADHDchildren who had normal reading skills were employed as a control group,and their EEGs compared to EEGs of 119 children with reading disorders(some of whom had a co-morbid diagnosis of ADD/ADHD). Subjects included86 males and 33 females between the ages of 7.5 and 12 years. Coherenceanalysis of EEG data is an additional method of quantitative analysisemployed in a smaller number of studies, with equally inconclusivefindings. Coherence analysis involves a cross-correlation that measuresthe relationship of activity at one site of the brain to another. In oneof the largest studies procured to date, Chabot and Serfontein [30]tested 407 children with attention deficits with and withouthyperactivity, with and without learning problems, children withattention problems who failed to reach DSM-III criteria for thedisorder, and 310 controls (ages 6-17). They first employed spectralanalysis and observed patterns of excess theta in frontal regions andincreased alpha (relative power) in the posterior regions for theclinical groups versus controls. They then employed coherence analysisand reported that one-third of the non-control children showed signs ofinterhemispheric dysregulation characterized by this pattern ofexcessive theta/alpha power in the right temporal and premotor (frontal)areas.

Overall, although numerous studies have examined ADHD versus non-ADHDchildren using EEG, techniques in study design vary widely. Of thestudies above, sixty percent involve only male subjects, eight of elevenstudies used electrode caps for EEG acquisition, and only three employeda clinical control group in addition to a normal control group. Onlyseven of the studies specifically evaluate EEGs of diagnosed ADHDchildren (versus children displaying attentional deficits and nohyperactivity). Of these studies, five did report increased theta waveactivity. However, these findings were not consistently found to involvesimilar brain regions (two in frontal region, one parietal region, oneanterior region, and one all sites). Two of the seven studies reporteddecreased alpha wave activity, while two reported increased alpharelative power, and the remaining three reported no significant alphawave findings. Again, of the seven studies involving ADHD diagnosedsubjects, one reported decreased absolute beta in the posterior regions,one reported decreased relative beta in the posterior regions, onereported decreased beta in the right frontal region, two reportedincreased beta wave activity, and two reported no significant betafindings. The presence of theta and the absence of beta may be theneural substrate of the inability to shift between tasks in order tofocus on the task at hand. This is affirmed in recent papers thathypothesize that an ADHD individual has difficulty in responding to thetarget task, not difficulty with ignoring peripheral stimuli [31].Overall, the differences in EEG spectra between affected and unaffectedchildren remain inconsistent and nonspecific enough to prevent their useas a diagnostic tool. In fact, in their 1993 review, Goldstein andIngersoll [38] concluded that consistent differences in EEG have notbeen documented between those with and without ADHD.

There is therefore a need in the art for a method and apparatus forassessing attentional impairments of persons. The present inventionprovides a method for evaluating and quantifying comprehensive data frompersons with attentional disorders. This data includes EEG informationwhen transitioning from one cognitive task to another, behavioralinformation, cognitive performance, and history of symptoms. The data isexamined within a sequential stochastic procedure, and used to diagnosisattentional disorders and evaluate treatment response.

BRIEF SUMMARY OF INVENTION

The present invention relates to the assessment of individuals withvarious attentional impairments, and assessing the treatment thereof,using EEG data.

In particular, a first aspect of the present invention is directed to amethod, apparatus, and computer useable medium for assessing individualsfor disorders associated with attentional impairments. The relatedmethod comprising (a) placing at least one electrode at a respectivecranial site on an individual, (b) obtaining digitized EEG data atepochs of a plurality of frequency bands, the EEG data being collectedfrom a first cognitive task period, a rest period, and a secondcognitive task period (wherein the individual performs predeterminedtasks during the first and second cognitive task periods, and theindividual rests during the rest period, (c) processing the EEG data todetermine electrophysical power (pW) obtained from the first cognitivetask period and the second cognitive task period, (d) calculating thepower change distance (PCD) between the first and second cognitive taskperiods, (e) filtering the PCD data by comparing the PCD data with anoise threshold number, (f) applying a cutoff frequency dividing thefiltered PCD data into two ranges, a first range being PCD data belowthe cut-off frequency and a second range being PCD data above thecut-off frequency, (g) calculating a Consistency Index wherein theConsistency Index is defined by the absolute value of the differencebetween a sum of the below cut-off PCD data and a sum of the above cutoff PCD data, and (h) comparing the Consistency Index to a control groupdatabase to provide the assessment of the individual.

Another aspect of the present invention is directed to a method,apparatus, and computer useable medium for assessing individuals fordisorders associated with attentional impairments of the individuals.The related method comprising (a) placing at least one electrode at arespective cranial site on an individual, (b) obtaining digitized EEGdata at epochs of alpha frequency, the EEG data collected from at leastone sequence of a first cognitive task period, a rest period, and asecond cognitive task period (wherein the individual performspredetermined tasks during the first and second cognitive task periods,and the individual rests during the rest period), (c) processing the EEGdata to determine electrophysical power (pW) for a sequence of alphapowers (α₁, α₂, . . . α_(k)) obtained from at least one of the firstcognitive task period, the reset period, and the second cognitive taskperiod, (d) calculating an Alpha Blockade Index (ABI) for the determinedalpha powers (α₁, α₂ . . . α_(k)), and (e) comparing the ABI to acontrol group database to provide the assessment of individual.

An additional aspect of the present invention is directed to a method,apparatus, and computer useable medium for assessing individuals fordisorders associated with attentional impairments of the individuals.The related method comprising (a) assigning an individual a probabilityof attentional impairment for demographic assessment, (b) assigning anindividual a probability of attentional impairment for psychometricassessment, (c) assigning an individual a probability of attentionalimpairment for Consistency Index, (d) assigning an individual aprobability of attentional impairment for Alpha Blockade Index (ABI),(e) calculating conditional probabilities of assigned attentionalimpairment for at least one of steps (a) through (d), whereby thecalculated conditional probability account for an assigned probabilityof an alternative step, and wherein the conditional probabilitiesprovide an overall probability or range of probability for theindividual, and (f) comparing the overall conditional probability orrange of conditional probability to a control group database to providethe assessment of the individual.

In yet another aspect of the present invention, there is provided amethod, apparatus, and computer useable medium for assessing thetreatment that individuals receive for disorders associated withattentional impairments of the individuals. The related methodcomprising (a) assigning an individual a probability of attentionalimpairment for Consistency Index (b) assigning an individual aprobability of attentional impairment for Alpha Blockade Index (ABI),(c) calculating conditional probabilities of assigned attentionalimpairment for at least one of steps (a) through (b), whereby thecalculated conditional probability account for an assigned probabilityof an alternative step, and wherein the conditional probabilitiesprovide an overall probability or range of probability for theindividual; and (d) repeat steps (a) through (c) a desired number oftimes over a select duration to compare the success or efficacy oftreatment.

These four aspects of the invention can be integrated together toprovide a comprehensive, flexible, and effective diagnostic measure.

These and other objects, along with advantages and features of theinvention disclosed herein, will be made more apparent from thedescription, drawings and claims that follow.

BRIEF SUMMARY OF THE DRAWINGS

The foregoing and other objects, features, and advantages of the presentinvention, as well as the invention itself, will be more fullyunderstood from the following description of preferred embodiments, whenread together with the accompanying drawings, in which:

FIGS. 1(A)-1(B) are graphical representations of the EEG frequencydimension, illustrating the EEG power spectrum for two cognitive tasksfor a consistent EEG transition case and an inconsistent EEG transitioncase, respectively.

FIGS. 2(A)-1(B) are graphical representations of the mean differences ofthe power spectra from task-to-task as recorded in FIGS. 1(A)-1(B).

FIGS. 3(A)-3(B) are graphical representations of the EEG spatialdimension for the various location of the electrodes for a consistentEEG transition case and an inconsistent EEG transition case,respectively.

FIGS. 4(A)-(B) are the graphical representations of the filtered set ofPCD for a consistent EEG transition case and an inconsistent EEGtransition case, respectively.

FIGS. 5 and 6 are results from pilot studies I and III, respectively,graphically presenting the CI for both a control group and the ADHDsubjects.

FIGS. 7 and 8 are results from pilot study IV, and graphically presentthe sequences of alpha-powers for a person without and with ADHD,respectively, that reflect high and low ABI.

FIG. 9 is a schematic illustration of the multidimensional individualprofile of the present invention stochastic classification method.

FIG. 10 is a schematic illustration of the stochastic transition linkingat least some of the steps illustrated in FIG. 9.

FIG. 11 graphically illustrates the probablity density function for animpaired attention group and the control group as determined from astochastic model analysis.

FIG. 12 graphically illustrates the assessment of treatmenteffectiveness for treatment procedures.

FIG. 13 is a functional block diagram for an illustrative computersystem for implementation of the present invention.

DETAILED DESCRIPTION OF THE INVENTION Consistency Index

The present invention is based on research conducted by the inventorsthat focused on ADHD in children and the EEG Consistency Index (CI)—ameasure based on subjects' EEG shift when going from one cognitive taskto another. Cumulatively, the inventors' studies demonstrated that: i)the CI discriminates, with almost no overlap, ADHD male subjects fromcontrols; ii) the CI correlates significantly with psychometric measuresof ADHD, and iii) the CI is reliable over time and is positivelyinfluenced by Ritalin. The inventors have introduced the ConsistencyIndex as a new measure of EEG alterations related to ADHD and in severalstudies showed that, on the same data, it works better than thepreviously known measures. One reason for that is that the ConsistencyIndex is computed on EEG differences between two tasks, and thereforecancels out “noise” inherent in the EEG measurement. The ConsistencyIndex is a measure based on a mathematical model of EEG changes duringtransitions from one cognitive task to another. The hallmark componentof attentional disorders is the inability to shift or transition betweentasks (McDonald, et al., 1999) [32]. The CI is the first defined EEGdiagnostic marker of attentional disorders and provides a new tool forassessment of ADHD, and additional disorders characterized by impairmentof attention.

The original CI (“CI 1”) is measured while subjects perform multiplealternating cognitive tasks (10-35 minutes) such as watching videos orreading, with rest periods with eyes open (5 minutes) in between.

A first aspect of the present invention provides inter alia theenhancement of diagnostic accuracy of the EEG CI by employing a secondversion of the Consistency Index (“CI2”), and furthermore this newmeasure (“CI2”) can add significantly to the EEG classification ofindividuals with ADHD, especially for boys under the age of 16.Specifically, findings to date indicate that a form of the ConsistencyIndex using the sum of the absolute differences (labeled CI2 todifferentiate it from the original CI), consistently differentiatedcontrols from ADHD subjects, with a high specificity (only 3 out of 20controls misclassified) and provided good sensitivity (only 3 out of 16ADHD subjects misclassified). The CI2 is calculated from the same dataobtained during the procedure defined for the CI1. The inventors haveaccumulated data for over 150 ADHD/non-ADHD subjects which provides animproved perspective of the problems so as to arrive at the followingconclusions (and unless otherwise stated, the CI discussed hereaftershall be considered the CI2 subject matter):

(1) Attentional deficits influence EEG records in a different way atdifferent levels of resolution in terms of time. More precisely: powerof Theta and Engagement Index are measured on a temporal scale of theorder of milliseconds; Consistency Index is measured on a time scale ofthe order of 10 minutes. At this time there is no assessment of EEGalterations measured in any intermediate scale. Also proposed is that arapid succession of approximately 1-minute cognitive tasks successfullydifferentiates adults with attentional deficits from adults withoutattention deficits. The rapid succession technique shall be discussedlater in this document as a second aspect of the present invention.

(2) Any one of the existing measures is not powerful enough to make aclear distinction between individuals with and without attentionaldeficits (with the Consistency Index being currently the best). However,a carefully weighted combination of several measures in the form of asequential stochastic model would work best for assessment of cognitiveand attentional disorders. The sequential stochastic model shall bediscussed later in the document as a third aspect of the presentinvention.

With the increase in specificity and sensitivity related toincorporating the C12 (first aspect of the present invention) with theCI1, the ability of the rapid succession of 1-minute cognitive tasks tosuccessfully differentiate adults with attentional deficits from adultswithout attention deficits (second aspect of the present invention), andthe incorporation of additional disorder specific psychometrics andratings to provide the sequential stochastic procedure (third aspect ofthe present invention), the present invention can be utilized as anaccurate and comprehensive assessment tool to diagnose and quantifychange in attentional and cognitive impairment.

Regarding the first aspect of the present invention, in a preferredembodiment the procedure uses standard EEG equipment and a standardelectrode cap with electrode placement according to the standard 10-20system. Example of EEG type systems are illustrated in Monastra, et al.U.S. Pat. No. 6,097,980; Heyrend et al. U.S. Pat. No. 6,044,292, John etal., U.S. Pat. No. 5,549,118; Tansey, U.S. Pat. No. 5,406,957; and John,U.S. Pat. No. 5,287,859; and are hereby incorporated by reference hereinin their entirety. The technician needs to demonstrate first that theimpedance of all electrodes is below 5 k ohms. Then, the dataacquisition protocol for computing the Consistency Index (CI) comprisesa subject reviewing a video for approximately ten minutes. This willinvolve the subject selecting from a library of age appropriatevideotapes a film of their choice to view for twenty minutes. The datafrom the first ten minutes will be considered adaptation period and willbe discarded. Next, the subject takes about a five-minute break. Thiscan be a brief unstructured break and electrode resistance check.Subjects are asked to keep their eyes open and remain still. Thereafter,the subject may take about a ten-minute reading test. The readingportion of the test will involve the subject silently reading for aboutten continuous minutes from a book of their choice that is within theirreading ability level.

The present invention model is based on the concept that the EEG datastream can be represented by a three-dimensional numeric array (at anygiven moment one dimension is frequency of brain waves), another isspatial (the location of the electrode on a subject's head), and thethird is time. ADHD can cause inconsistency in the frequency or spatialdimension or in both when shifting across cognitive tasks.

The present invention EEG assessment procedure begins with a standardEEG data acquisition/transformation sequence: The raw EEG data aredigitized amplitudes sampled about 200 times a second through scalpelectrodes. A Fast Fourier transformation (FFT) is used to compute thepower spectrum of the data, epoch by epoch. One skilled in the art wouldappreciate that various modes for computing the transform may beemployed besides the FFT depending of factors such as the type of signalbe analyzed, the available processing capability, etc. For example, butnot limited thereto, the invention may employ the Fourier Transform(FT), Short-Time FT (STFT), Discrete Cosine Transforms (DCT), or wavelettransforms (WT). The frequencies represented in this spectrum are,depending on the filter settings, between 0.5-2 and 80-100 Hz. Thisregion includes four basic EEG frequency bands: Delta (0.5-4 Hz), Theta(4-8 Hz), Alpha (8-13 Hz) and Beta (13-22 Hz). Separately recorded (andgenerally not included in further analysis) is High Beta+EMG (22-40 Hz)and the residual power, carried by frequencies above 40 Hz. This pictureis scanned by a number of EEG electrodes at different locations of asubject's head. The basic CI uses only 8 electrodes, F3, F4, CZ, PZ, C3,C4, P3, P4, however the present invention model is flexible enough toaccommodate experiments with other locations, such as FZ, F7, F8, P7,P8, T7, T8. The FFT is updated epoch by epoch at one-second increments.Thus, during testing each person gets a three-dimensional(frequency-location-time) power spectrum representation.

Using a series of figures depicting the sequential steps of thecomputation of the CI, the present invention shall be discussed further.

FIGS. 1(A)-1(B) are graphical representations of the EEG frequencydimension, illustrating the EEG power spectrum for two cognitive tasksfor a consistent EEG transition case and an inconsistent EEG transitioncase, respectively. These graphs present the basis of the concept of aconsistent EEG in the frequency dimension. The black line is the powerspectrum of a subject performing a task; the gray line is the powerspectrum of the same subject while performing an adjacent task. In FIG.1(A) the black line is above the gray line at lower frequencies andmostly below the gray line at higher frequencies (above 16 Hz). Thisshows that a shift from one task to another (from black to gray) resultsin an increase of higher frequencies and a decrease of lowerfrequencies. In contrast, in FIG. 1(B) no specific change in thefrequency distribution over is observed.

The term “consistent” is best defined by looking at mean differences ofpower spectra from task-to-task (See FIG. 2(A)). As shown in FIG. 2(A),this difference is mostly positive at lower frequencies and mostlynegative at higher frequencies. As shown in FIG. 2(B), the powerdifferences are scattered below and above the frequency axis. Visually,a consistent shift between two tasks will be presented by anuninterrupted domain (FIG. 2(A)) while an inconsistent shift wouldresult in sporadic power changes along the EEG spectrum, as in FIG.2(B).

FIGS. 3(A)-3(B) are graphical representations of the EEG spatialdimension for the various location of the electrodes. As opposed to thefrequency dimension, the presentation of spatial EEG consistency isbased on a discrete presentation of the power spectrum at several EEGchannels. FIGS. 3(A)-3(B) presents an 8-channel (electrode) setting andspatially consistent/inconsistent shifts between two tasks. Thecontinuous spectrum at each electrode is integrated into (four in thisexample) frequency bands. A consistent shift would mean that at aparticular frequency band at most channels will display similar,unidirectional readings (FIG. 3(A)), while an inconsistent shift willresult in scattered power changes across the electrode sites (FIG.3(B)). An alternative embodiment will use 21-channel EEG system, whichchanges the data retrieval software, but not the general idea of spatialEEG consistency. It would be appreciated that any quantity of channelsmay be utilized.

The EEG consistency, as shown FIGS. 1-3, is used as a basis for thedevelopment of the present invention related algorithm and software thatcomputes the CI. In a preferred embodiment the algorithm works asfollows.

1) Discrete spectra, including residual power, are calculated for allEEG channels through a standard EFT algorithm;

2) Power change distances (PCD) between two contiguous tasks arecomputed for each EEG band and channel according to equation no. 1.1PCD=M1−M2 SD1 2N1+SD2 2N2  (1)

Initially, each PCD is normalized using the formula of equation no. 1,where M1 and M2 are the mean powers at two contiguous tasks, SD1 and SD2are their standard deviation, and N1 and N2 are the epoch counts atthese tasks. Normalization allows changes in one channel/frequency bandto be directly comparable to another;

3) PCD undergo filtering to eliminate changes below a “noise threshold.”The noise threshold, presented by horizontal lines in FIGS. 3(A)-3(B),works as follows: The PCD that are larger by an absolute value than thethreshold will be marked with 1 or −1 depending on their direction,while all PCD below threshold will be marked by zero. In FIGS. 3(A)-3(B)noise thresholds of .+−.1.65 are presented by the line designated as NT.These thresholds transform the PCD of FIGS. 3(A)-3(B) into a sequence of1, 0, −1 that indicates, for each EEG band and channel, whether asignificant power change was observed while the person shifted from onetask to another. Since the PCD have a distribution close to Studentt-distribution, a threshold of 1.65 is equivalent to making anone-tailed t-test comparing the average EEG power at Task 1 and 2 atp=0.05. Once again, this is just an association provided to clarify ourmethods. No conclusions based on t-test or any other parametrictechnique are involved in the computation of the CI. The noise thresholdvalue is adjustable and for an alternative embodiment the noisethreshold is 3.5.

4) The filtered set of PCD is presented in FIGS. 4(A)-(B). The shiftfrom task one to task two would be consistent if most of the filteredPCD below some cutoff frequency are positive, while most of theindicators above this cutoff frequency are negative, or vice versa. Incontrast, the shift would be inconsistent if the PCD vary greatly bymagnitude and/or sign. Thus, FIGS. 4(A)-(B) present a consistent and aninconsistent EEG, respectively, at a cutoff frequency between beta andhigh beta, as denoted by the line CF.

5) The final pass of the computation is an addition of the filtered PCDbelow and above the cutoff value. The CI is defined as the absolutevalue of the difference between these two sums, expressed as apercentage, i.e., computed using equation no. 2 below:2CI=100 1 N (belowcutoff i−abovecutoff j)% where i,j=−1,0,1  (2)

For example, in FIG. 4(A) there is provided a sum of 13 below the cutoffand a sum of −5 above the cutoff. Thus, the CI of the consistent shiftpresented in FIG. 4(A) will be 18. In contrast the CI of theinconsistent shift in FIG. 4(B) will be 1 (0 below the cutoff and −1above the cutoff). The maximum CI equals the number of EEG channelsmultiplied by the number of EEG bands used during spectrumdiscretization. For example, with 8-channel EEG equipment and 4 bandsthe CI ranges from 0 to 32. In order to make the results comparableacross different experiments, the CI will be expressed in terms ofpercentage from its maximum value. For example, the CI in FIG. 4(A) willbe 56.25 percent, while in FIG. 4(B) it will be 3.125 percent.

An alternative embodiment of the CI can be computed as the sum ofabsolute values of the PCD. This version has properties similar to theCI and in some studies has shown superior discrimination ability.

Alpha Blockage Index

A second aspect of the present invention shall provide a method,apparatus, and computer program product for providing intermediatetemporal scale readings monitored in the form of training to decreasealpha blockade during the rapid succession of approximately 1-minutecognitive tasks. Such a method may be used for neurofeedback toimplement improved attentional abilities.

With respect to monitoring alpha blockade and providing neurofeedback,in traditional EEG, activities of the waking EEG in alpha frequencieshave special significance in that they form the “alpha rhythm,” aposteriorly-dominant activity that attenuates (or “blocks”) with eyeopening. This rhythm first emerges at age 3-4 month and graduallyincreases in frequency until adult levels are attained in latechildhood. Since the alpha rhythm is slowed or absent during heightenedanxiety or extremely low arousal such as drowsiness, attaining alphaenhancement (increasing power of alpha) is more difficult for bothover-aroused subjects (such as ADHD subjects) and for under-arousedsubjects (again, such as ADHD subjects or other persons suffering frominattention). Control subjects have demonstrated a significantdifference in the power of alpha, with a higher power during rest and alower power of alpha during cognitive tasks. Since alpha enhancementresults from reduction of alpha blocking influences, on-lineneurofeedback of the power of alpha can be presented to the subject withattention difficulties, with the instructions to facilitate relaxationwhen at rest. Instructions to concentrate when shifting to the cognitivetask can also be used to facilitate alpha blocking, thus inverselyincreasing the power of beta, associated with mental concentration andfocus.

Similarly, neuropsychological EEG studies have attributed certainchanges in powers of frequency band under specific testing conditions.The presence of beta activity is considered by most psychophysiologiststo reflect active mental processing, whereas alpha is associated withrelaxation and delta and theta with underarousal. The attenuation ofalpha and theta activity and the presence of beta activity signify“active mental processing” in these paradigms [33]. Therefore,neurofeedback in the intermediate scale of a few minutes will beprovided regarding increasing alpha during rest periods and suppressingalpha when engaged in cognitive tasks.

Regarding the Alpha Blockade Index (ABI) EEG data acquisition procedure,the raw EEG data are digitized amplitudes sampled about 200 times asecond through scalp electrodes and FFT is used to compute the powerspectrum of the data, epoch by epoch. The power of Alpha frequencies(8-13 Hz) is computed for all epoch and then averaged within every taskand rest period and across the EEG electrodes. Thus, for each personthere is obtained a sequence of alpha powers α₁, α₂, . . . α_(k),corresponding to the sequence of rapidly changing tasks and restperiods.

In particular, the subjects will engage in a cognitive task for about1-2 minutes requiring concentrated cognitive effort, (e.g. tracking on acomputer screen). Next, the subjects are then asked to take a break forabout 1 minute, while they keep their eyes open and remain still.Thereafter, the subjects resume their cognitive tasks stated above forabout 1-2 minutes. This series may then be repeated comprisingalternating tasks and breaks for up to about 12 trials (approximatelyone hour) or other desired level of repetition.

The ABI may also be calculated as a step in the sequential stochasticmodel (to be discussed later as a third aspect of the presentinvention). Such ABI data will also provide on-line feedbackapproximately every one to two minutes indicating the power of alphaduring task and rest. The objective will be for the subjects to increasetheir power of alpha during rest and minimize the power of alpha whileperforming the cognitive tasks.

The ABI for a person during the rapid transition protocol is computed asfollows:3ABI=100k−1i=2k i−i−1max(i−1,i)  (3)

Similarly to the CI, AI ranges from 0 to 100 and, as the inventors datashow, a lower AI is a sign of ADHD.

The CI and ABI result from a complex mathematical model and theircomputation is not straightforward. No standard statistical procedurescan be applied to compute the CI, or ABI, and clearly they cannot becalculated manually. In a preferred embodiment, the present software iswritten in Java and has the following features: i) interface for readingdata from 4- 8- and 21-channel EEG equipment, ii) capability ofcomputing for any combination of threshold and cut-off parameters, andiii) processing of a list of subjects, and combining their indices intoa suitable data base for further analysis.

The method and apparatus of the present invention (as discussedthroughout this document) may be implemented using hardware, software ora combination thereof and may be implemented in one or more computersystems or other processing systems, or partially performed inprocessing systems such as personal digit assistants (PDAs). In anexample embodiment, the invention was implemented in software running ona general-purpose computer 1300 as illustrated in FIG. 13. Computersystem 1300 includes one or more processors, such as processor 1304.Processor 1304 is connected to a communication infrastructure 1306(e.g., a communications bus, crossover bar, or network). Computer system1300 includes a display interface 1302 that forwards graphics, text, andother data from the communication infrastructure 1306 (or from a framebuffer not shown) for display on the display unit 1330.

Computer system 1300 also includes a main memory 1308, preferably randomaccess memory (RAM), and may also include a secondary memory 1310. Thesecondary memory 1310 may include, for example, a hard disk drive 1312and/or a removable storage drive 1314, representing a floppy disk drive,a magnetic tape drive, an optical disk drive, etc. The removable storagedrive 1314 reads from and/or writes to a removable storage unit 1318 ina well-known manner. Removable storage unit 1318, represents a floppydisk, magnetic tape, optical disk, etc. which is read by and written toby removable storage drive 1314. As will be appreciated, the removablestorage unit 1318 includes a computer usable storage medium havingstored therein computer software and/or data.

In alternative embodiments, secondary memory 1310 may include othermeans for allowing computer programs or other instructions to be loadedinto computer system 1300. Such means may include, for example, aremovable storage unit 1322 and an interface 1320. Examples of suchremovable storage units/interfaces include a program cartridge andcartridge interface (such as that found in video game devices), aremovable memory chip (such as a ROM, PROM, EPROM or EEPROM) andassociated socket, and other removable storage units 1322 and interfaces1320 which allow software and data to be transferred from the removablestorage unit 1322 to computer system 1300.

Computer system 1300 may also include a communications interface 1324.Communications interface 1324 allows software and data to be transferredbetween computer system 1300 and external devices. Examples ofcommunications interface 1324 may include a modem, a network interface(such as an Ethernet card), a communications port, a PCMCIA slot andcard, etc. Software and data transferred via communications interface1324 are in the form of signals 1328 which may be electronic,electromagnetic, optical or other signals capable of being received bycommunications interface 1324. Signals 1328 are provided tocommunications interface 1324 via a communications path (i.e., channel)1326. Channel 1326 carries signals 1328 and may be implemented usingwire or cable, fiber optics, a phone line, a cellular phone link, an RFlink and other communications channels.

In this document, the terms “computer program medium” and “computerusable medium” are used to generally refer to media such as removablestorage drive 1314, a hard disk installed in hard disk drive 1312, andsignals 1328. These computer program products are means for providingsoftware to computer system 1300. The invention includes such computerprogram products.

Computer programs (also called computer control logic) are stored inmain memory 1308 and/or secondary memory 1310. Computer programs mayalso be received via communications interface 1324. Such computerprograms, when executed, enable computer system 1300 to perform thefeatures of the present invention as discussed herein. In particular,the computer programs, when executed, enable processor 1304 to performthe functions of the present invention. Accordingly, such computerprograms represent controllers of computer system 1300.

In an embodiment where the invention is implemented using software, thesoftware may be stored in a computer program product and loaded intocomputer system 1300 using removable storage drive 1314, hard drive 1312or communications interface 1324. The control logic (software), whenexecuted by the processor 1304, causes the processor 1304 to perform thefunctions of the invention as described herein.

In another embodiment, the invention is implemented primarily inhardware using, for example, hardware components such as applicationspecific integrated circuits (ASICs). Implementation of the hardwarestate machine to perform the functions described herein will be apparentto persons skilled in the relevant art(s).

In yet another embodiment, the invention is implemented using acombination of both hardware and software.

In an example software embodiment of the invention, the methodsdescribed above were implemented in Java, but could be implemented inother program languages, such as C++, that would be appreciated by thoseskilled in the art.

Studies Verifying the CI and the ABI

Described below are the findings from three pilot studies that involveddifferent subjects, different age groups, different genders, with datacollection by different research assistants, in different facilities,using different EEG equipment. In summary, the results indicate that theCI: clearly differentiates ADHD from control subjects and correctlyclassifies over 80% of all subjects; discriminates, with almost nooverlap, ADHD male subjects (age <16 years) from controls; correlatessignificantly with psychometric measures of ADHD; and is reliable overtime and is positively influenced by methylphenidate.

Meta-analysis of the data: A total of 67 subjects, 33 ADHD and 34control, participated in the pilot studies that we conducted and in astudy conducted at Sweet Briar College. The sample consisted of 38 malesand 29 females; 43 subjects were younger that 16 years. Analysis ofvariance with independent factors ADHD versus Control, Gender and Agegroup revealed that:

The average CI of ADHD subjects is 29% vs. 50% for controls, F=43.7,p<0.0001;

There was a significant Gender effect with males having a higher CI,F=4.1, p<0.05;

There was an age trend with younger subjects having higher CI, F=3.7,p=0.06;

There was a significant interaction between ADHD-control and Gendereffects with males displaying stronger CI differences between ADHD andcontrols, F=5.6, p<0.05.

On the basis of the CI, a logistic regression model classified correctly82% of all ADHD subjects and 77% of all control subjects with an overallclassification accuracy of 80%. This model was statisticallysignificant, p<0.0001. The classification power of the logistic modelincreased to 90% if only younger male subjects were included in theanalysis. In addition, a Boolean decision-making rule based on the CIclassified all but one of younger ADHD boys versus theirage-gender-matched controls, a 96% correct classification.

From this analysis we can conclude that the CI is a highly significantdiscriminant of ADHD versus control subjects. In addition, with thespecific subgroup of younger males it works extremely well oncase-by-case basis classifying accurately almost 100% of these subjectsin our pilot studies. This latter finding and the fact that youngermales below age of 16 are the predominant ADHD population dictated ourdecision to describe this invention as a tool for screening anddiagnosis boys, ages 8-16.

Detailed Results from Inventors' Pilot Studies

Study I: Referring to table I below and FIG. 5, four boys, ages 6-10,with ADHD and four age-matched control boys tested at two 30-minutetrials (video and reading) separated by a 5-minute break. For the ADHDboys, this procedure was repeated three months later, to assesstest-retest reliability [34].

1 TABLE I ADHD ID CI % SI Control 101 100 2 102 75 0 107 75 0 108 94 2ADHD 103 44 10 104 25 8 105 12 23 106 25 22 Group comparison: p=0.0015Correlation with ADHD SI: r=0.84

Study II: Six ADHD males and six non-ADD) males, ages 18-25,participated in a double-blind, placebo versus methylphenidatecontrolled crossover design study. The subjects were given four tasks ofthe Gordon Diagnostic System, two easy (auditory and visual) and twohard (auditory and visual). Results have been submitted for publicationin Cox, et al. [35]; and Merkel, et al. [36].

Study III: Referring to Table II below and FIG. 6, eighteen boys andseventeen girls, ages 8-16, classified as either ADHD or non-ADHD (9boys and 8 girls with ADHD and 9 boys and 9 girls without ADHD) weretested for 36 minutes while performing various tasks (10 min. video, 1min. break, 10 min. reading, 5 min. break, 10 min. math). The completeresults have been submitted for publication in Kovatchev et al. [37].

2 TABLE II Boys Girls ID CI % ID CI % Control 106 91 101 72 107 75 10447 108 41 105 25 109 47 119 35 112 94 120 47 115 56 12153 117 75 129 31127*0 137 9 128 94 141 87ADHD 102 25 114 0 111 19 126 40 116 3 131 31123 22 134 59 124 3 135 0 125 28 138 34 130 44 139 0 132 16 140 0 1360*Identified by teacher as ADHD. Group comparison, boys: p=0.0008 Groupcomparison, girls: p=0.03 Correlation with ADHD SI: r=0.67

New Data and Recent Analyses

Study IV: EEG data for 30 female college students with ADHD and 30female control college students with no ADHD tested on and offmethylphenidate. Twelve data sets are included in the analysis below. Inaddition to higher CI, the analysis of the series of short tasksseparated by short rest periods performed in the second half of the datacollection revealed previously unknown, but very significantinconsistencies in the EEGs of female college students with ADHDrelative to non-ADHD controls. ADHD subjects had (i) Less elevated alphaactivity (vigilance) during rest periods, 20.0.+−.1.3 vs. 26.8.+−.1.0,p<0.001, and (ii) Less suppressed alpha activity during tasks,15.9.+−.0.4 vs. 14.5.+−.0.6, p<0.001. These new findings resulted in theformulation of the Alpha Blockade Index. FIGS. 7 and 8 present thesequences of alpha-powers for a person without and with ADHD,respectively, that reflect high and low ABI.

Study V: In the study sponsored by the Commonwealth Health ResearchBoard (CHRB), 77 children (67 males and 10 females, 36 ADHD and 41non-ADHD) were administered EEGs. The comparison of ADHD versus controlyielded a Consistency Index of 47% for the ADHD subjects and aConsistency Index of 65% for the control subjects, F=9.0, p<0.005. Thisconfirmed our primary hypothesis that lower Consistency Index isassociated with ADHD.

The optimal classification threshold of the Consistency Index was foundto be 40%, which confirmed the results from our pilot studies (e.g. aConsistency Index of 40% or less is considered to be a sign of ADHD).Twenty out of 30 ADHD boys had CI₁ of 40% or less, which implies thatthe Consistency Index confirmed 66% of the initial diagnosis of ADHD.Thirty out of 33 controls had CI₂ above 40%, which implies that theConsistency Index had over 90% specificity. These results meet ourexpectations that the Consistency Index would confirm most non-ADHD boysand would reject some of the initial diagnoses of ADHD (one-third of thecases in this study). The latter confirms our hypothesis that thedetermination of ADHD based solely on background questionnaires andinterviews may be resulting in over-diagnosis of the disorder.

Study VI: Having an objective, reliable diagnostic procedure could alsobe used to assess the effectiveness of treatment in persons with ADHD.This could be achieved by demonstrating whether the treatment beingconsidered appropriately impacts on the EEG parameters of concern.

In the most recent study funded by McNeil Consumer Health Care, theinventors evaluated six males with ADHD or ADD, both on and offmethylphenidate. This study was conducted to examine the effects ofmedication upon driving ability, but the inventors also collected EEGdata for subjects while on and off medication. Subjects were between theages of 16-19, and reported a previous positive response tomethylphenidate. Four of 6 ADHD subjects obtained a Consistency Index(CI) of 40% or lower when taking no medication. One subject was sleepyduring the no medication EEG and obtained a CI of 100, which ispredicted with this state. Therefore, his CI from the no medicationtrial is most likely invalid. Significantly, all subjects displayed anincrease in their CI when tested on Ritalin, and all but one subjectachieved a CI of 50% or more when on Ritalin. A CI of 50% or more isassociated with normal or consistent cognitive transition, and isconsidered to be indicative of the absence of ADHD or ADD.

This study confirmed the hypotheses that lower EEG consistency duringtransitions from one cognitive task to another (Consistency Index of<40%) will be a significant physiological marker associated withindividuals with ADHD or ADD. That the EEG CI will increase or normalize(CI.gtoreq.50%) in individuals with ADHD or ADD who are treated withappropriate doses of methylphenidate.

Stochastic Model

A third aspect of the present invention shall provide a method,apparatus, and computer program product for providing a sequential ornon-sequential stochastic model procedure that would be utilized todiagnose attentional disorders, provide neurofeedback treatment, andevaluate treatment response. The present invention sequential stochasticprocedure is an optimization of several components used to diagnose ormark attentional or cognitive deficits or impairments. These componentsinclude several sequential assessments, some of which would be diseasespecific, and some of which would be general to allattentional/cognitive impairment: a) psychometric data (in vivo and byhistory); b) behavioral data; c) EEG acquisition involving assessment ofCI1 and CI2 (to access large temporal scale): and d) EEG acquisitioninvolving rapid succession of 1-minute cognitive tasks (to accessintermediate temporal scale). Each of the sequential steps contributesto the assessment of the condition and the final diagnosis is based uponthe combination of all or substantially all.

Summarily and as set forth immediately below, the algorithms comprise,but not limited thereto the following procedures integrated by asequential stochastic classification model:

a) Psychometric assessment—standardized test protocol for screening andevaluation of persons for presence/absence of symptoms of cognitiveattentional impairment:

i) Standard ADHD psychometrics.

ii) Psychometrics evaluating the ability for attentional shifts,specifically the Assessment of Cognitive Transition Difficulty (ACTD),and related neuropsychological assessments.

b) Electroencephalographic (EEG) data acquisition procedure comprised oftwo sessions:

i) Cognitive Transition Protocol (“Transition Protocol”), consisting ofabout two 10-minute cognitive tasks separated by about a 5-minutestructured rest.

ii) Rapid Cognitive Transition Protocol (“Rapid Transition Protocol”),consisting of a rapid sequence of changing about 2-minute tasksseparated by about 1-minute rests.

c) Mathematical model of EEG transition consistency for ADHD based onEEG acquisition:

i) Transition Protocol Consistency Index (CI) for quantifying the lackof attentional transition between the tasks, corresponding to the slowpart above. The CI has two versions distinguished by different summationformulas as described previously.

ii) Rapid Transition Protocol Alpha Blockade Index (ABI) for quantifyingless elevated alpha activity (vigilance) during rest periods,corresponding to the fast part of the data collection.

d) EEG Data analysis algorithms and software that follow this model.

e) Stochastic assessment procedure merging the psychometrics, the EEGdata, and the results form the mathematical models into a singlediagnostic instrument.

The present invention method pertains directly to enhancement ofexisting psychological, behavioral, and physiological EEG dataacquisition systems by introducing a sequential stochastic modelprocedure, and an intelligent data interpretation component capable ofassessing EEG inconsistencies associated attentional impairments.Potential users of this product will be any person or organization thatdiagnoses or treats persons with attentional or cognitive impairments.Upon approval, the method can be used for initial screening anddiagnosis of disorders associated with impaired attention, such as ADHD,as well as for treatment and evaluation of the effects of treatments,such as medication or additional therapies.

Regarding the demographic assessment, the method includes standarddemographic questions such as age, gender, etc., and surveys aboutfamily and school environment.

Psychometric data includes standard ADHD scales and specificallydeveloped questionnaires that measure attentional transitions. Forexample, for standard psychometrics, psychological data regardinggeneral attention (i.e., MMSE) is obtained, and then data regardingdisease or disorder specific cognitive/attentional impairments isobtained via assessment questionnaires, scales, and inventories specificto the disorder, (i.e., the DuPaul Rating Scale for ADHD). In addition,neuropsychological findings (such as the results of the PASAT), as wellas behavioral ratings (in vivo) are incorporated. Whereas forpsychometrics that evaluates the ability for attentional shifts, dataregarding the difficulty of cognitive transition is obtained via theAssessment of Cognitive Transition Difficulty (ACTD).

Turning to the present invention stochastic assessment procedure, thismodel employs several sequential dependent assessments to increase theprobability of a reliable and valid diagnosis of attentional impairment.Each dependent measure is geared towards gathering pertinent dataspecific to the particular domain (psychological, behavioral,physiological), so that all domains are assessed and predictive validityis maximized. In the next step, the physiological aspects of the EEGdata are obtained, calculated and incorporated into the model. These EEGmeasures include the CI and ABL as measured by alpha blockade duringmultiple alternating minute long cognitive tasks. Thus, the finaldiagnosis/assessment is based upon the mathematical combination of allof the above psycho-physiological data, and therefore, has increasedspecificity/sensitivity beyond any single measure.

Formal data representation. Turning to FIG. 9, the data for eachsubject, the subject's individual profile 950, is represented as avector comprising personal and demographic/environmental information905, psychometric scores 910, CI 915, and ABI 920. Thus, each subject isrepresented as a point in a multi-dimensional space, corresponding tothe coordinates of the individual profile vector 950.

Diagnostic Assessment Algorithm

The basic unit of the algorithm is a single step assigning a personalprobability for attentional impairment based on the assessment of thepersonal profile at each specific step as follows. It should beappreciated that the probability ranges as set forth below in steps 1-4are intended to be illustrated rather than limiting and other desiredranges may be implemented practiced as well. The steps can be practicedin alternate orders than as listed below.

Step 1—Demographic Assessment.

For the demographic data this will be done based on populationprevalence data in different sub-populations defined by age, gender,etc. At this step every subject is assigned a probability of attentionalimpairment, PI 925. For example, a 19 year old female will receive priorprobability of attentional impairment p=0.005 while an 8 year old boywill be assigned probability 0.12. In general the demographic assessmentwill be used to establish prior probabilities of attentional impairmentfor each subject.

Step 2—Psychometric assessment.

Pertaining to psychometrics, a probability for attentional impairment P₂930 will be assigned based on the standard scales and the Assessment ofCognitive Transition Difficulty (ACTD) in the following manner: p=1.0,for a definitive attentional impairment classification, p=0.0 for adefinitive non-impairment classification. P=0.5 for unclear cases.

Step 3—Consistency Index.

Pertaining to the CI, the probability for attentional impairment P₃ 935at this step is p=0 if CI>60%; p=1 if CI<40%, and p=0.5 otherwise.

Step 4—Alpha Blockade Index.

The probability attentional impairment P₄ 940 at this step is p=0 ifAI>40%; p=1 if CI<20%, and p=0.5 otherwise.

In addition, referring to FIG. 10, the above steps are linked bycomputing the conditional probabilities of attentionalimpairment/non-impairment at each step, given the assessment at aprevious or posterior step(s). It is contemplated that some steps may beomitted from the linking.

Turning to FIG. 11, FIG. 11 graphically illustrates the probabilitydensity function for an impaired attention group 1120 and the controlgroup 1130 as determined from a stochastic model analysis. Since at eachstep we have a “gray zone” of a non-definitive assessment 1140, thefinal result of the sequential computations will be a probability thatthe assessed person has attentional impairment. FIG. 11 illustrates thedistributions of these probabilities for attentionalimpairment/non-impairment populations. The distributions are expected tooverlap, thus identifying a subgroup of individuals with no definitivediagnosis. However, at each iterative or repetitive step of theassessment, the overlap zone becomes smaller and the final result is anassessment that is substantially more precise than any of its individualsteps.

Treatment Assessment Algorithm

A major disadvantage of the psychometric criteria is that they do notprovide means for immediate assessment of the effectiveness of atreatment. In contrast, the results from both the present inventionCognitive Transition and Rapid Cognitive Transition EEG protocols areavailable within minutes (the Fast EEG protocol provides even almoston-line tracking of attentional shifts) and therefore the CI and the ABIcan be used as indicators of the effectiveness of a treatment procedure.

As illustrated in FIG. 12, the Treatment Assessment Algorithm includesthe last two steps of the Diagnostic Assessment Algorithm embedded intorecursive loop containing the treatment. The algorithm evaluates shiftsin the probability of attentional impairment that may result from thetreatment. A successful treatment would increase the personalprobability for non-impairment, shifting the attentional impairmentprobability distribution of FIG. 11 toward the non-impairment zone.Measuring such shifts allows the present invention method, apparatus,and computer program product to: 1) immediately assess treatmenteffectiveness, 2) compare treatments (since the output is standardized),3) evaluate duration of treatment effects (provided that the assessmentis performed several times throughout the course of treatment), and 4)the Rapid Transition EEG protocol and the ABI provide on-line feedbackand thus opportunity for biofeedback-based treatment procedures.

In conclusion, an advantage of the present invention is that itprovides, among other things, a standardized test protocol for screeningand evaluation of attentional impairment. That includes a combination ofpsychological and physiological assessments. The present inventionmethod and apparatus is built upon the notion that most significantmarkers of attentional impairment arise when subjects shift theirattention from one task to another, and that this phenomenon can bequantified by a combination of psychometrics and measures derived fromEEG data. Preliminary studies suggest that the method is most precisefor screening and diagnosis of ADHD among boys, 8 to 16 years of age,but is also effective for other gender age groups, including adolescentmales and college females.

Another advantage of the present invention is that it provides method,apparatus, and computer program product that pertains directly to theenhancement of existing psychological, behavioral, and physiological EEGdata acquisition systems by introducing a sequential stochastic modelprocedure, and an intelligent data interpretation component capable ofassessing EEG inconsistencies associated attentional impairments.Potential users of this product will be any person or organization thatdiagnoses or treats persons with attentional or cognitive impairments.Upon approval, the present invention method can be used for initialscreening and diagnosis of disorders associated with impaired attention,such as ADHD, as well as for treatment and evaluation of the effects oftreatments, such as medication or additional therapies.

Further yet, the present invention will provide a relatively simplediagnostic procedure that will lead to better screening and treatment ofattentional impairment, and the prevention of overmedication. It willfurther provide an inexpensive and clear method for diagnosis of ADHSand other impairments.

Finally, the present invention provides a comprehensive, flexible, andan effective diagnostic measure of attentional abilities, as well as anindicator for treatment effectiveness and rehabilitation progress.

The invention may be embodied in other specific forms without departingfrom the spirit or essential characteristics thereof. The foregoingembodiments are therefore to be considered in all respects illustrativerather than limiting of the invention described herein. Scope of theinvention is thus indicated by the appended claims rather than by theforegoing description, and all changes which come within the meaning andrange of equivalency of the claims are therefore intended to be embracedherein.

REFERENCES

The following articles, publications, patent applications, and patentsare hereby incorporated by reference in their entirety herein:

-   1. Ritchie, K., Artero, S., & Touchon, J., 2001. Classification    criteria for mild cognitive impairment: A population based    validation study. Neurology, 56(1), 37-42.-   2. Ballard, C, O'Brien, J. Gray, A., Cormack, F., Ayre, G.,    Rowan, E. H., Thompson, P., Bucks, R., McKeith, I., Walker, M., &    Tovee, M., 2001. Attention and Fluctuating Attention in Patients    with Dementia with Lewy Bodies and Alzheimer Disease. Archives of    Neurology, 58(6), 977-982.-   3. Grodstein, F., Chen, J., Wilson, R., & Manson, J., 2001. Type 2    Diabetes and Cognitive Function in Community Dwelling Elderly Women.    Diabetes Care. 24(6), 1060-1065.-   4. Sohlberg, M. & Mateer, C., 2001. Improving Attention and Managing    Attentional Problems Adapting Rehabilitation Techniques to Adults    with ADD. Annals of New York Academy of Sciences, 931, 359-375.-   5. Armstrong, C., Hayes, K, & Martin R., 2001. Neurocognitive    Problems in Attention Deficit Disorder: Alternative Concepts and    Evidence for Impairment in Inhibition of Selective Attention. Annals    of New York Academy of Sciences, 931, 196-215.-   6. Levin, H., Rossman, R., Rose, J., et al., 1979. Long-term    neuropsychological outcome of closed head injury. Journal of    Neurosurgery, 50, 412-422.-   7. Meyer, J., Rauch, G., Rauch, R., Haque, A., & Crawford, K., 2000.    Cardiovascular and Other Risk Factors for Alzheimer's Disease and    Vascular Dementia. Annals of New York Academy of Sciences, 903,    411-423.-   8. Chang, L, Speck, O., Miller, E., Braun, J., et al., 2001. Neural    correlates of attention and working memory deficits in HIV patients.    Neurology, 57(6), 1001-1007.-   9. ADHD, NIH Consensus Statement, 1998. Diagnosis and Treatment of    Attention Deficit Hyperactivity Disorder. November 16-18.-   10. Pohjasvaara, T., Ylikoski, R., Leskela, M., et al., 2001.    Evaluation of Various Methods of Assessing Symptoms of Cognitive    Impairment and Dementia. Alzheimer Disease and Associated Disorders,    15(4), 184-193.-   11. Lezak, M., Neuropsychological Assessment, Second Edition. New    York: Oxford University Press, 1995.-   12. Rosen, W., Mohs, R., & Davis, K., 1984. A new rating scale for    Alzheimer's disease. American Journal of Psychiatry, 141, 1356-1364.-   13. Doraiswamy, P., Kaiser, L., Bieber, F., & Garman, R., 2001. The    Alzheimer's Disease Assessment Scale: Evaluation of Psychometric    Properties and Patterns of Cognitive Decline in Multicenter Clinical    Trials of Mild to Moderate Alzheimer's Disease. Alzheimer Disease    and Associated Disorders. 15(4), 174-183.-   14. MacArthur, J., Hoover, D., Bacellar, H., et al., 1993. Dementia    in AIDS patients: incidence and risk factors. Neurology, 42,    2245-2252.-   15. MacArthur, J., Cohen, B., Slenes, O., et al., 1989. Low    prevalence of neurological and neuropsychological abnormalities in    other wise healthy HIV-1 infected individuals: Results from the    Multicenter AIDS Cohort Study. Annals of Neurology, 26, 601-611.-   16. Heaton, R., Grant, I., Butters, N., et al., 1995. The HNRC    Neuropsychology of HIV infection at different stages. HIV    Neurobehavioral Research Center. Journal of International    Neuropsychology Society, 58, 231-251.-   17. American Psychiatric Association, 1994. Diagnostic and    statistical manual of mental disorders (4th ed.). Washington, D.C.:    American Psychiatric Association.-   18. Goldman, L. S., Genel, M., Bezman, R. J., and Slanetz, P. J.    (1998). Council report of diagnosis and treatment of    Attention-Deficit Hyperactivity Disorder in children and    adolescents. Journal of the American Medical Association, 279,    1100-1107.-   19. Wolraich, M. L. & Baumgaertel, A., 1996. The prevalence of    Attention Deficit Hyperactivity Disorder based on the new DSM-IV    criteria. Peabody Journal of Education, 71, 168-186).-   20. Hebert, L., Beckett, L., Scherr, P., & Evans, D., 2001. Annual    Incidence of Alzheimer Disease in the United States Projected to the    Years 2000 through 2050. Alzheimer Disease and Associated Disorders,    15(4), 169-173).-   21. Barkley, R. A., Guevremont, D. C., Anastopoulos A. D., DuPaul G.    J., & Shelton T. L., 1993. Driving-related risks and outcomes of    attention deficit hyperactivity disorder in adolescents and young    adults: a 3- to 5-year follow-up survey. Pediatrics, 92, 212-218.-   22. Zametkin, A. J. & Rapoport, J. L., 1987. Neurobiology of    attention deficit disorder with hyperactivity: where have we come in    50 years? Journal of the American Academy of Child and Adolescent    Psychiatry, 26, 676-686.-   23. Crawford, H. & Barabasz, M., 1996. Automated EEG magnitudes in    children with and without attention deficit disorder during    neurological screening and cognitive tasks. Child Study Journal, 26,    71-86.-   24. Dykman, R. A., Holcomb, P. J., Oglesby, D. M., & Ackerman, P.    T., 1982. Electrocortical Frequencies in Hyperactive,    Learning-Disabled, Mixed, and Normal Children. Biological    Psychiatry, 17(6), 675-685).-   25. Satterfield, J., Schell, A., Backs, R., & Hidaka, K., 1984. A    Cross-sectional and longitudinal study of age effects of    electrophysiological measures in hyperactive and normal children.    Biological Psychiatry, 19(7), 973-990).-   26. Mann, C. A., Lubar, J. F., Zinmmerman, A. W., Miller, C. A.,    Muenchen, R. A., 1992. Quantitative analysis of EEG in boys with    attention-deficit-hyperactivity disorder: Controlled study with    clinical implications. Pediatric Neurology, 8, 30-36.-   27. Janzen, T., Graap, K., Stephanson, S., Marshall, W.,    Fitzsimmons, G., 1995. Differences in baseline EEG measures for ADD    and normally achieving preadolescent males. Biofeedback and Self    Regulation, 20, 65-82.28. Clarke, A. R., Barry, R. J., McCarthy, R.,    Selikowitz, M., 1998. EEG analysis in    attention-deficit/hyperactivity disorder: A comparative study of two    subtypes. Psychiatry Research, 81, 19-29.-   29. Ackerman, P. T., Dykrnan, R. A., Oglesby, D. M., Newton, J. E.    O., 1994. EEG power spectra of children with dyslexia, slow    learners, and normally reading children with ADD during verbal    processing. Journal of Learning Disabilities, 27 (10), 619-630.-   30. Chabot, R. & Serfontein, G., 1996. Quantitative    Electroencephalographic profiles of children with attention deficit    disorder. Biological Psychiatry, 40 (10), 951-963.-   31. Douglas, V., 1998. Cognitive control processes in ADHD. In Quay,    H., Hogan, A., (Eds.) Handbook of Disruptive Behavior Disorders.-   32. McDonald, S., Bennett, K., Chambers, H., and Castiello,    U., 1999. Covert orienting and focusing of attention in children    with attention deficit hyperactivity disorder. Neuropsychologia, 37    (3), 345-356.-   33. Andreassi, J. L., 1989. Psychophysiology: Human behavior and    physiological response. Hillsdale, N.J.: Erlbaum.-   34. Cox, D. J., Kovatchev, B. P., Morris, J. B., Philips, C., Hill,    R., and Merkel, L. (1999). Electroencephalographic and Psychometric    Differences Between Boys With and Without Attention    Deficit/Hyperactivity Disorder (ADHD)—A Pilot Study. Applied    Psychophysiology and Biofeedback, 23: 179-188.-   35. Cox, D. J., Merkel, R. L., Kovatchev, B. P., and Seward, R.    Attention Deficit/Hyperactivity Disorder (ADHD) Impairs Driving    Performance, which is Remedied with Stimulant Medication: A    Preliminary Double Blind Placebo Controlled Trial. Applied    Psychophysiology and Biofeedback. In press.-   36. Merkel, R. L., Cox, D. J., Kovatchev, B. P., Morris, J., Seward,    R., Hill, R., and Reeve, R. (2000). The EEG Consistency Index as a    measure of Attention Deficit/Hyperactivity Disorder and    responsiveness to medication: A Double blind placebo controlled    pilot study. Journal of Applied Psychophysiology. In press.-   37. Kovatchev, B. P., Hill, R., Morris, J. B., Reeve, R., Robeva, R.    S., Loboschefski, T., and Cox, D. J. EEG Transition Consistency and    its Relationship to ADHD: Validation of the Consistency Index. In    press.-   38. Goldstein, S., & Ingersoll, B., 1993. Controversial treatments    for children with ADHD and impulse disorders. In Handbook of    childhood impulse disorders and ADHD: Theory and practice. C. C.    Thomas Publisher, Springfield: Ill.

1. A method for assessing individuals for disorders associated withattentional impairments of the individuals, said method comprising: a)placing at least one electrode at a respective cranial site on anindividual; b) obtaining digitized EEG data at epochs of alphafrequency, said EEG data collected from at least one sequence of a firstcognitive task period, a rest period, and a second cognitive taskperiod, wherein: the individual performs predetermined tasks during saidfirst and second cognitive task periods, and the individual rests duringsaid rest period; c) processing said EEG data to determineelectrophysical power (pW) for a sequence of alpha powers (α₁, α₂, . . .α_(k)) obtained from at least one said first cognitive task period, saidreset period, and said second cognitive task period; d) calculating anAlpha Blockade Index (ABI) for said determined alpha powers (α₁, α₂, . .. α_(k)); and e) comparing said ABI to a control group database toprovide the assessment of individual.
 2. The method of claim 1, whereincalculating said ABI in step (d) is calculated using the followingformula:${A\; B\; I} = {\frac{100}{k - 1} \cdot {\sum\limits_{i = 2}^{k}{{\frac{\alpha_{i} - \alpha_{i - 1}}{\max\left( {\alpha_{i - 1},\alpha_{i}} \right)}}.}}}$3. The method of claim 1, wherein: said first cognitive task period hasa duration within a range of about 1 minute to about 2 minutes; saidrest period has a duration of about 1 minute; and said second cognitivetask period has a duration within a range of about 1 minute to about 2minutes.
 4. The method of claim 1, wherein the individual during saidfirst and second cognitive task periods participate in an activityselected from the group consisting of: reading, performing math, viewingvideo, tracking on a computer screen, and listening.
 5. The method ofclaim 1, wherein said Alpha frequency ranges from about 8 to about 14Hz.
 6. The method of claim 1, wherein number of electrodes range from 1to
 15. 7. The method of claim 1, wherein the disorders include at leastone of mild cognitive impairment (MCI) in individuals with pre-dementia,dementia, dementia with Lewy bodies, Alzheimer's Disease, traumaticbrain injury, Attention Deficit/Hyperactivity Disorder (ADHD), andcognitive/attentional declines associated with chronic diseases such asdiabetes, cardiovascular disease, and HIV infection.
 8. A method forassessing individuals for disorders associated with attentionalimpairments of the individuals, said method comprising: a) assigning anindividual a probability of attentional impairment for demographicassessment; b) assigning an individual a probability of attentionalimpairment for psychometric assessment; c) assigning an individual aprobability of attentional impairment for Consistency Index; d)assigning an individual a probability of attentional impairment forAlpha Blockade Index (ABI); e) calculating conditional probabilities ofassigned attentional impairment for at least one of steps (a) through(d), whereby said calculated conditional probability account for anassigned probability of an alternative step, and wherein saidconditional probabilities providing an overall probability or range ofprobability for the individual; and f) comparing said overallconditional probability or range of conditional probability to a controlgroup database to provide the assessment of the individual.
 9. Themethod of claim 8, wherein said demographic assessment is based onfactors including at least one of gender, age, presence of co-morbidpsychological or learning disorders, and first degree relative diagnosedwith attentional impairment.
 10. The method of claim 8, wherein saidpsychometric assessment is based on factors including at least one ofmini-mental status examination, the Assessment of Cognitive TransitionDifficulty (ACTD), Continuous Performance Tests (CPT), anddisorder-specific rating scale.
 11. The method of claim 8, wherein saidConsistency Index assessment is based on the calculated ConsistencyIndex of step (g) of claim
 1. 12. The method of claim 8, wherein saidAlpha Blockade Index assessment is based on the following formula:${A\; B\; I} = {\frac{100}{k - 1} \cdot {\sum\limits_{i = 2}^{k}{{\frac{\alpha_{i} - \alpha_{i - 1}}{\max\left( {\alpha_{i - 1},\alpha_{i}} \right)}}.}}}$13. A method for assessing the treatment that individuals receive fordisorders associated with attentional impairments of the individuals,said method comprising: a) assigning an individual a probability ofattentional impairment for Consistency Index; b) assigning an individuala probability of attentional impairment for Alpha Blockade Index (ABI);c) calculating conditional probabilities of assigned attentionalimpairment for at least one of steps (a) through (b), whereby saidcalculated conditional probability account for an assigned probabilityof an alternative step, and wherein said conditional probabilitiesproviding an overall probability or range of probability for theindividual; and d) repeat steps (a) through (c) a desired number oftimes over a select duration to compare the success or efficacy oftreatment.
 14. The method of claim 13, wherein the duration for whichsteps (a) through (c) are repeated can be at least one of hourly, daily,weekly, monthly, yearly, and any fraction or multiple thereof.
 15. Anapparatus for assessing individuals for disorders associated withattentional impairments of the individuals, said apparatus comprising:an EEG device having at least one electrode adaptive for attachment at arespective cranial site on an individual, said EEG device obtainingdigitized EEG data at epochs of a plurality of frequency bands, said EEGdata collected from a first cognitive task period, a rest period, and asecond cognitive task period, wherein: the individual performspredetermined tasks during said first and second cognitive task periods,and the individual rests during said rest period; a processor programmedto: a) processing said EEG data to determine electrophysical power (pW)for a sequence of alpha powers (α₁, α₂, . . . α_(k)) obtained from atleast one said first cognitive task period, said reset period, and saidsecond cognitive task period; b) calculating an Alpha Blockade Index(ABI) for said determined alpha powers (α₁, α₂, . . . α_(k)); and c)comparing said ABI to a control group database to provide the assessmentof individual.
 16. The apparatus of claim 15, wherein calculating saidABI in step (c) is calculated using the following formula:${ABI} = {\frac{100}{k - 1} \cdot {\sum\limits_{i = 2}^{k}{{\frac{\alpha_{i} - \alpha_{i - 1}}{\max\left( {\alpha_{i - 1},\alpha_{i}} \right)}}.}}}$17. The apparatus of claim 15, wherein: said first cognitive task periodhas a duration within a range of about 1 minute to about 2 minutes; saidrest period has a duration of about 1 minute; and said second cognitivetask period has a duration within a range of about 1 minute to about 2minutes.
 18. The apparatus of claim 15, wherein the individual duringsaid first and second cognitive task periods participate in an activityselected from the group consisting of: reading, performing math, viewingvideo, tracking on a computer screen, and listening.
 19. The apparatusof claim 15, wherein said Alpha frequency ranges from about 8 to about14 Hz.
 20. The apparatus of claim 15, wherein number of electrodes rangefrom 1 to
 15. 21. The apparatus of claim 15, wherein the disordersinclude at least one of mild cognitive impairment (MCI) in individualswith pre-dementia, dementia, dementia with Lewy bodies, Alzheimer'sDisease, traumatic brain injury, Attention Deficit/HyperactivityDisorder (ADHD), and cognitive/attentional declines associated withchronic diseases such as diabetes, cardiovascular disease, and HIVinfection.
 22. An apparatus for assessing individuals for disordersassociated with attentional impairments of the individuals, saidapparatus comprising: a) means for assigning an individual a probabilityof attentional impairment for demographic assessment; b) means forassigning an individual a probability of attentional impairment forpsychometric assessment; c) means for assigning an individual aprobability of attentional impairment for Consistency Index; d) meansfor assigning an individual a probability of attentional impairment forAlpha Blockade Index (ABI); e) means for calculating conditionalprobabilities of assigned attentional impairment for at least one ofsteps (a) through (d), whereby said calculated conditional probabilityaccount for an assigned probability of an alternative step, and whereinsaid conditional probabilities providing an overall probability or rangeof probability for the individual; and f) means for comparing saidoverall conditional probability or range of conditional probability to acontrol group database to provide the assessment of the individual. 23.The apparatus of claim 22, wherein said demographic assessment is basedon factors including at least one of gender, age, presence of co-morbidpsychological or learning disorders, and first degree relative diagnosedwith attentional impairment.
 24. The apparatus of claim 22, wherein saidpsychometric assessment is based on factors including at least one ofmini-mental status examination, the Assessment of Cognitive TransitionDifficulty (ACTD), Continuous Performance Tests (CPT), anddisorder-specific rating scale.
 25. The apparatus of claim 22, whereinsaid Alpha Blockade Index assessment is based on the calculated ABI ofstep (b) of claim
 15. 26. An apparatus for assessing the treatment thatindividuals receive for disorders associated with attentionalimpairments of the individuals, said apparatus comprising: a) means forassigning an individual a probability of attentional impairment forConsistency Index; b) means for assigning an individual a probability ofattentional impairment for Alpha Blockade Index (ABI); c) means forcalculating conditional probabilities of assigned attentional impairmentfor at least one of steps (a) through (b), whereby said calculatedconditional probability account for an assigned probability of analternative step, and wherein said conditional probabilities providingan overall probability or range of probability for the individual; andd) means for repeat steps (a) through (c) a desired number of times overa select duration to compare the success or efficacy of treatment. 27.The apparatus of claim 26, wherein the duration for which steps (a)through (c) are repeated can be at least one of hourly, daily, weekly,monthly, yearly, and any fraction or multiple thereof.
 28. A computerprogram product comprising computer useable medium having computerprogram logic for enabling at least one processor in a computer systemto assess individuals for disorders associated with attentionalimpairments based on digitized EEG data, said digitized EEG dataobtained at epochs of a plurality of frequency bands from an individual,said EEG data collected from a first cognitive task period, a restperiod, and a second cognitive task period, wherein the individualperforms predetermined tasks during said first and second cognitive taskperiods, and the individual rests during said rest period, said computerlogic comprising: a) processing said EEG data to determineelectrophysical power (pW) for a sequence of alpha powers (α₁, α₂, . . .α_(k)) obtained from at least one said first cognitive task period, saidreset period, and said second cognitive task period; b) calculating anAlpha Blockade Index (ABI) for said determined alpha powers (α₁, α₂, . .. α_(k)); and c) comparing said ABI to a control group database toprovide the assessment of individual.
 29. A computer program productcomprising computer useable medium having computer program logic forenabling at least one processor in a computer system to assessindividuals for disorders associated with attentional impairments basedon digitized EEG data, said digitized EEG data obtained at epochs of aplurality of frequency bands from an individual, said EEG data collectedfrom a first cognitive task period, a rest period, and a secondcognitive task period, wherein the individual performs predeterminedtasks during said first and second cognitive task periods, and theindividual rests during said rest period, said computer logiccomprising: a) assigning an individual a probability of attentionalimpairment for demographic assessment; b) assigning an individual aprobability of attentional impairment for psychometric assessment; c)assigning an individual a probability of attentional impairment forConsistency Index; d) assigning an individual a probability ofattentional impairment for Alpha Blockade Index (ABI); e) calculatingconditional probabilities of assigned attentional impairment for atleast one of steps (a) through (d), whereby said calculated conditionalprobability account for an assigned probability of an alternative step,and wherein said conditional probabilities providing an overallprobability or range of probability for the individual; and f) comparingsaid overall conditional probability or range of conditional probabilityto a control group database to provide the assessment of the individual.30. A computer program product comprising computer useable medium havingcomputer program logic for enabling at least one processor in a computersystem to assess the treatment that individuals receive for disordersassociated with attentional impairments based on digitized EEG data,said digitized EEG data obtained at epochs of a plurality of frequencybands from an individual, said EEG data collected from a first cognitivetask period, a rest period, and a second cognitive task period, whereinthe individual performs predetermined tasks during said first and secondcognitive task periods, and the individual rests during said restperiod, said computer logic comprising: a) assigning an individual aprobability of attentional impairment for Consistency Index; b)assigning an individual a probability of attentional impairment forAlpha Blockade Index (ABI); c) calculating conditional probabilities ofassigned attentional impairment for at least one of steps (a) through(b), whereby said calculated conditional probability account for anassigned probability of an alternative step, and wherein saidconditional probabilities providing an overall probability or range ofprobability for the individual; and d) repeat steps (a) through (c) adesired number of times over a select duration to compare the success orefficacy of treatment.